The present application relates to web traffic analytics and more particularly to methods and systems for allowing one to customize the type of analytics tracked through a graphic user interface.
Programs for analyzing traffic on a network server, such as a worldwide web server, are known in the art. One such prior art program is described in U.S. Pat. No. 6,925,442 for a Method and Apparatus for Evaluating Visitors to a Web Server, which is co-owned with the present application and incorporated herein by reference for all purposes. In these prior art systems, the program typically runs on the web server that is being monitored. Data is compiled, and reports are generated on demand—or are delivered from time to time via email—to display information about web server activity, such as the most popular page by number of visits, peak hours of website activity, most popular entry page, etc.
Analyzing activity on a worldwide web server from a different location on a global computer network (“Internet”) is also known in the art. To do so, a provider of remote web-site activity analysis (“service provider”) generates JavaScript code that is distributed to each subscriber to the service. The subscriber copies the code into each web-site page that is to be monitored, or alternately sends the code to the visitor computer as an external include by embedding a GET command into the web page that then retrieves the additional tracking code from an external server.
When a visitor to the subscriber's web site loads one of the web-site pages into his or her computer, the JavaScript code collects information, including time of day, visitor domain, page visited, etc. The code then calls a server operated by the service provider—also located on the Internet—and transmits the collected information thereto as a URL parameter value. Information is also transmitted in a known manner via a cookie.
Each subscriber has a password to access a page on the service provider's server. This page includes a set of tables that summarize, in real time, activity on the customer's web site.
The above-described arrangement for monitoring web server activity by a service provider over the Internet is generally known in the art. Examples of the information analyzed includes technical data, such as most popular pages, referring URLs, total number of visitors, returning visitors, etc. The basic mechanism of such services is that each tracked web-site page contains some JavaScript in it that requests a 1×1 image from the service provider's server. Other information is sent along with that request, including a cookie that uniquely identifies the visitor. Upon receipt of the request, the service provider records the hit and stages it for full accounting. This is a proven method for tracking web site usage.
The type of information that can be tracked is nearly limitless. Every additional piece of information tracked necessarily increases the size of the code used to track this information. As a result, users wanting to track a huge amount of visitor information incur a large overhead on code downloaded with the web page or sent as an external include.
A problem arises when customizing the data mining code for different subscribers. That is, one subscriber may want to track many different aspects of a web site visitor's interaction with a web site, while another might just want the basics such as what browser the visitor is using and at what resolution the visitor has set their display device. Two conventional methods have been employed to service these different types of subscribers: (1) a service provider would provide a single piece of monolithic code to all subscribers that accounts for and reports every possible data to be tracked but ignores the unneeded data in the tracking process, or (2) a service provider provides a basic level of service that tracks the minimal data, and manually programs additional pieces as necessary to track the additional data specified by each individual subscriber. The first method has the disadvantage of requiring all subscribers to attach a large piece of code to their web pages, and also generating much data that will go unused. The second method, while more efficient for the subscriber, incurs additional overhead to the service provider by requiring each tracking program to be manually tailored to the customer.
Accordingly, the need still remains for simplifying the steps needed to custom tailor a web analytics program for the needs of different subscribers having different needs.